A novel hybrid approach based on dynamic adaptive variable-weight optimization for short-term wind speed prediction

2020 ◽  
Vol 12 (1) ◽  
pp. 016101
Author(s):  
Yuansheng Huang ◽  
Lei Yang ◽  
Yingqi Yang ◽  
Yulin Dong ◽  
Chong Gao
Keyword(s):  
Wind Speed ◽  
Hybrid Approach ◽  
Weight Optimization ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Variable Weight ◽  
Speed Prediction

A Gaussian process regression based hybrid approach for short-term wind speed prediction

2016 ◽  
Vol 126 ◽  
pp. 1084-1092 ◽  
Author(s):  
Chi Zhang ◽  
Haikun Wei ◽  
Xin Zhao ◽  
Tianhong Liu ◽  
Kanjian Zhang
Keyword(s):  
Wind Speed ◽  
Gaussian Process ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals A Hybrid Approach for Short-term Wind Speed Prediction in Huan County of China

2019 ◽  
Vol 295 ◽  
pp. 012030 ◽  
Author(s):  
Tonglin Fu ◽  
Mingxia Yang ◽  
Pengyue Ju ◽  
Kun Liu ◽  
Huani Zhao
Keyword(s):  
Wind Speed ◽  
Hybrid Approach ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

scholarly journals Local models-based regression trees for very short-term wind speed prediction

2015 ◽  
Vol 81 ◽  
pp. 589-598 ◽  
Author(s):  
A. Troncoso ◽  
S. Salcedo-Sanz ◽  
C. Casanova-Mateo ◽  
J.C. Riquelme ◽  
L. Prieto
Keyword(s):  
Wind Speed ◽  
Regression Trees ◽  
Local Models ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Speed Prediction

Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

2014 ◽  
Vol 511-512 ◽  
pp. 927-930
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Jin Huang ◽  
He Liu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Prediction ◽  
Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

Short-term wind speed prediction model based on GA-ANN improved by VMD

2020 ◽  
Vol 156 ◽  
pp. 1373-1388 ◽  
Author(s):  
Yagang Zhang ◽  
Guifang Pan ◽  
Bing Chen ◽  
Jingyi Han ◽  
Yuan Zhao ◽  
...  
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Speed Prediction
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